Citation

Yan X, Richards S, Su X. Accid. Anal. Prev. 2010; 42(1): 64-74.

Affiliation

Southeastern Transportation Center (STC), The University of Tennessee, Suite 309, Conference Center Building, Knoxville, TN 37996-4133, USA. xyan1@utk.edu

Copyright

(Copyright © 2010, Elsevier Publishing)

DOI

10.1016/j.aap.2009.07.003

PMID

19887146

Abstract

This paper applies a nonparametric statistical method, hierarchical tree-based regression (HTBR), to explore train-vehicle crash prediction and analysis at passive highway-rail grade crossings. Using the Federal Railroad Administration (FRA) database, the research focuses on 27 years of train-vehicle accident history in the United States from 1980 through 2006. A cross-sectional statistical analysis based on HTBR is conducted for public highway-rail grade crossings that were upgraded from crossbuck-only to stop signs without involvement of other traffic-control devices or automatic countermeasures. In this study, HTBR models are developed to predict train-vehicle crash frequencies for passive grade crossings controlled by crossbucks only and crossbucks combined with stop signs respectively, and assess how the crash frequencies change after the stop-sign treatment is applied at the crossbuck-only-controlled crossings. The study results indicate that stop-sign treatment is an effective engineering countermeasure to improve safety at the passive grade crossings. Decision makers and traffic engineers can use the HTBR models to examine train-vehicle crash frequency at passive crossings and assess the potential effectiveness of stop-sign treatment based on specific attributes of the given crossings.

Language: en